flowchart LR
subgraph s1["compute recreational potential"]
C[("cropping of the 4<br>components file to the<br>extent of interest")]
E["Compute each component's<br>contribution"]
F["Rescale to unit interval"]
end
A("Definition of the<br><i>persona preferences</i>") ---> E
B("Definition of the<br><i>area of interest</i>") --> C
C --> E
E --> F
F --> G["recreational potential spatRast"]
style s1 fill:#FFF9C4
style A color:#000000
style B color:#000000
style C color:#000000
style E color:#000000
style F color:#000000
style G color:#000000
BioDT - The Recreational Model
1. Scope of the Model
The recreational potential model (RP Model) was developed as part of the Cultural Ecosystem Services prototype Digital Twin (CES-pDT) workpackage within the BioDT project. The (CES-pDT) has two independent core models:
the Biodiversity model (developed by Simon Rolph and Dylan Carbone, UK Centre for Ecology & Hydrology, Wallingford, United Kingdom): aims at estimating biodiversity levels across mammals, birds, plant and insects.
the Recreational Potential model (developed by Jan Dick, Chris Andrews, Maddalena Tigli, Megan Williams and Joe Marsh Rossney, UK Centre for Ecology & Hydrology, Edinburgh, United Kingdom): aims at estimating the landscapes’ capacity to provide opportunities for outdoor recreation based on varying user interests.
This report specifically documents the Recreational Potential (RP) Model, detailing its methodology, data sources, operational mechanisms, and outlining areas for future refinement and development.
CHRISTOPHER TO ADD MORE INFO? MAYBE THERE ARE SOME LIT SOURCES USED?
2. The model
2.1. Model description
The Recreational Potential (RP) Model estimates the recreational value of landscapes using four key components, each represented by a raster file:
Landscape Component (SLSRA.tif)
Natural Features Component (FIPS_N.tif)
Infrastructure Component (FIPS_I.tif)
Water Component (Water.tif)
Each of the four component raster files contains multiple layers, with each layer representing a specific feature. Each raster covers the entire domain of the model (with a 20mx20m resolution), currently Scotland (see 2.3. The underlying data).
The values in these raster layers range from 0 to 1, interpreted as follows:
1 indicates that the feature is present in that cell.
0 or NAs indicates that the feature is absent from that cell
Values between 0 and 1 are present in the features/layers of the Infrastructure and Water components rasters, and reflect the proximity from nearby features. This continuous scale allows the RP model to incorporate how areas near e.g., a loch, can still contribute to the recreational potential, even if they do not directly contain the feature
The four rasters form the basis for calculating the Recreational Potential (RP) across the landscape.
To run the RP model the user must define:
Area of interest: defined using a bounding box (terra::ext() in R).
Persona preferences: a CSV file assigning a score (0–10) to each of the 87 features (see 2.3. The underlying data), reflecting the persona’s interest in each (see example persona file “presets.csv” ).
RP model processing steps
- Cropping the raster files
The model first crops each of the four component rasters to the specified area of interest. Using the terra package in R, the bounding box is converted to a vector polygon with terra::vect(), and then used to crop the raster via terra::crop().
- Calculating each components contribution
For each cell (20m x 20m), the model extracts values from all relevant layers (features), multiplies them by the corresponding persona score, and computes a weighted sum across layers. This results in one new raster per component that reflects the component’s contribution based on the persona preferences.
- Re-scale to unit interval
Each of the components contribution rasters is re-scaled using “min-max normalization”:
\[ scaled.value = (x-min)/mix-min \]
Minimum values are mapped as 0, maximum values are mapped as 1, and all the intermediate values are proportionally scaled.
- Combining the components RP
The four normalized component rasters are summed cell by cell. The resulting raster is then re-scaled again (min-max normalization) to produce the final Recreational Potential raster.
The model’s output is a SpatRaster object (that can be saved as a .tif) with five layers:
“SLSRA”: the RP of the landscape component
“FIPS_N”: the RP of the natural features component
“FIPS_I”: the RP of the infrastructure component
“Water”: the RP of the water component
“Recreational_Potential”: the combined RP
In all layers, values range from 0 to 1, where higher values (closer to 1) indicate greater recreational potential.
Because the model output is a result of the normalization of recreational potential of a specific area of interest, the values are relative within that area. This means that running the model with a different geographic extent will yield to different scores for the same features, even if the underlying data is unchanged.
2.2. Example run
We demonstrate the RP Model applied to the Bush Estate area (nearby Edinburgh) using two contrasting personas: a hard recreationalist and a soft recreationalist. The resulting maps illustrate how different preferences lead to different areas being highlighted for recreational value.
- Hard recreationalist exmaple
Here we show a model run the Bush Estate area using a “hard recreationalist” example of persona (saved as “Hard_Recreationalist” in “presets.csv” ). Key features preferences for this persona are:
| score_group | features |
|---|---|
| highest scores (scored 10 or 9) | Rock Walls (FIPS_N), Mountains (FIPS_N), Inland cliffs, rock pavements and outcrops (SLSRA), Rock cliffs, ledges and shores (SLSRA), National Park (SLSRA) |
| lowest scores (scored 0 or 1) | Built-up areas (FIPS_N), Flood plain (FIPS_N), Depressions (FIPS_N), No slope (FIPS_N), Gentle slope (FIPS_N), Country Park (SLSRA), Raised and blanket bog (SLSRA), Valley mires, poor fens and transition mires (SLSRA), Windthrown woodland (SLSRA), Woodland fringes and clearings and tall forb stands (SLSRA), Bare field or exposed soil (SLSRA), Built-up area (SLSRA), Royal Society for the Protection of Birds (RSPB) Reserve (SLSRA), Pond (Water), Motorway (FIPS_I), A Road (FIPS_I), B Road (FIPS_I), Minor or local road (FIPS_I), Access roads or Track (FIPS_I), Saltings (FIPS_N) |
This persona highly values remote, challenging environments and avoids built-up or highly managed areas.
- Soft recreationalist example
Here we show a model run the Bush Estate area using a “soft recreationalist” example of persona (saved as “Soft_Recreationalist” in “presets.csv”). Key features preferences for this persona are:
| score_group | features |
|---|---|
| highest scores (scored 10 or 9) | Traffic Free: Paved Surface (FIPS_I), Coastal dunes and sandy shore (SLSRA), National Park (SLSRA), Major Lochs (Water), Beaches or Dunes (FIPS_N), No slope (FIPS_N), Freshwater (SLSRA), Mixed deciduous and coniferous woodland (SLSRA), Scots pine woodland (SLSRA), Broadleaved deciduous woodland (SLSRA) |
| lowest scores (scored 0 or 1) | Rock Walls (FIPS_N), Built-up areas (FIPS_N), Rocks or Scree (FIPS_N), Depressions (FIPS_N), Extremely steep slope (FIPS_N), Screes (SLSRA), Windthrown woodland (SLSRA), Bare field or exposed soil (SLSRA), Unnamed minor stream or tributary (Water), Motorway (FIPS_I), A Road (FIPS_I), Saltings (FIPS_N) |
Soft recreationalists are drawn to tranquil, accessible landscapes and natural water features, avoiding steep or harsh terrain.
2.3. The underlying data
The methodology used to create each of the four components’ Scotland wide raster file is described in detail in this section.
An overview of the raster files of all four component for the Easter Bush area (the same extent displayed in the example model run) is also displayed.
| name raster | description |
|---|---|
| SLSRA.tif | Landscape component This includes data on land cover type, landscape designations and conservation, and farmland of high nature value. resolution: 20x20 |
| FIPS_N.tif | Natural Features component This includes data on land form types, soil types and slope. resolution: 20x20 |
| Water.tif | Water component This includes data on water feature types, as the presence of a lake or river. resolution: 20x20 |
| FIPS_I.tif | Infrastructure component This includes data on road and track, footpaths and cycle networks.
|
- Landscape component
CHRISTOPHER TO PROVIDE INFO ON THIS RASTRE FILE
| nr | Name | Description |
|---|---|---|
| 1 | SLSRA_CP_2 | Country Park |
| 2 | SLSRA_HNV_2 | Designated High Nature Value (HNV) farmland |
| 3 | SLSRA_LCM_1 | Alpine and subalpine grassland |
| 4 | SLSRA_LCM_2 | Arable land and market gardens |
| 5 | SLSRA_LCM_3 | Arctic, alpine and subalpine scrub |
| 6 | SLSRA_LCM_4 | Bare field or exposed soil |
| 7 | SLSRA_LCM_5 | Base-rich fens and calcareous spring mires |
| 8 | SLSRA_LCM_6 | Broadleaved deciduous woodland |
| 9 | SLSRA_LCM_7 | Built-up area |
| 10 | SLSRA_LCM_8 | Coastal dunes and sandy shore |
| 11 | SLSRA_LCM_9 | Coastal shingle |
| 12 | SLSRA_LCM_10 | Dry grassland |
| 13 | SLSRA_LCM_11 | Freshwater |
| 14 | SLSRA_LCM_12 | Inland cliffs, rock pavements and outcrops |
| 15 | SLSRA_LCM_13 | Lines of trees, small planted woodlands, early-stage woodland and coppice |
| 16 | SLSRA_LCM_14 | Littoral sediment or saltmarsh |
| 17 | SLSRA_LCM_15 | Mesic grassland |
| 18 | SLSRA_LCM_16 | Mixed deciduous and coniferous woodland |
| 19 | SLSRA_LCM_17 | Non-native coniferous plantation |
| 20 | SLSRA_LCM_18 | Raised and blanket bog |
| 21 | SLSRA_LCM_19 | Riverine and fen scrubs |
| 22 | SLSRA_LCM_20 | Rock cliffs, ledges and shores |
| 23 | SLSRA_LCM_21 | Scots pine woodland |
| 24 | SLSRA_LCM_22 | Screes |
| 25 | SLSRA_LCM_23 | Seasonally wet and wet grassland |
| 26 | SLSRA_LCM_24 | Temperate montane scrub |
| 27 | SLSRA_LCM_25 | Temperate shrub heathland |
| 28 | SLSRA_LCM_26 | Valley mires, poor fens and transition mires |
| 29 | SLSRA_LCM_27 | Windthrown woodland |
| 30 | SLSRA_LCM_28 | Woodland fringes and clearings and tall forb stands |
| 31 | SLSRA_NNR_2 | National Nature Reserve (NNR) |
| 32 | SLSRA_NP_2 | National Park |
| 33 | SLSRA_NR_2 | Nature Reserve |
| 34 | SLSRA_RP_2 | Regional Park |
| 35 | SLSRA_RSPB_2 | Royal Society for the Protection of Birds (RSPB) Reserve |
| 36 | SLSRA_SAC_2 | Special Area of Conservation (SAC) |
| 37 | SLSRA_SPA_2 | Special Protection Area (SPA) |
| 38 | SLSRA_SSSI_2 | Site of Special Scientific Interest (SSSI) |
| 39 | SLSRA_SWT_2 | Scottish Wildlife Trust Reserve |
| 40 | SLSRA_WLA_2 | Wild Land Areas |
- Natural Features component
CHRISTOPHER TO PROVIDE INFO ON THIS RASTRE FILE
| nr | Name | Description |
|---|---|---|
| 1 | FIPS_N_Landform_1 | Foothills |
| 2 | FIPS_N_Landform_2 | Mountains |
| 3 | FIPS_N_Landform_3 | Terraces |
| 4 | FIPS_N_Landform_4 | Flood plain |
| 5 | FIPS_N_Landform_5 | Beaches or Dunes |
| 6 | FIPS_N_Landform_6 | Rocks or Scree |
| 7 | FIPS_N_Landform_7 | Depressions |
| 8 | FIPS_N_Landform_8 | Hills |
| 9 | FIPS_N_Landform_9 | Lowlands |
| 10 | FIPS_N_Landform_10 | Rock Walls |
| 11 | FIPS_N_Landform_11 | Uplands |
| 12 | FIPS_N_Landform_12 | Valley sides |
| 13 | FIPS_N_Landform_13 | Valley bottom |
| 14 | FIPS_N_Landform_14 | Built-up areas |
| 15 | FIPS_N_Landform_15 | Saltings |
| 16 | FIPS_N_Landform_16 | Hummocks, mounds or moraines |
| 17 | FIPS_N_Slope_1 | No slope |
| 18 | FIPS_N_Slope_2 | Gentle slope |
| 19 | FIPS_N_Slope_3 | Medium slope |
| 20 | FIPS_N_Slope_4 | Steep slope |
| 21 | FIPS_N_Slope_5 | Very steep slope |
| 22 | FIPS_N_Slope_6 | Extremely steep slope |
| 23 | FIPS_N_Soil_1 | Peat or Organic |
| 24 | FIPS_N_Soil_2 | Mineral |
- Infrastructure component
| nr | Name | Description |
|---|---|---|
| 1 | FIPS_I_LocalPathNetwork_2 | Path |
| 2 | FIPS_I_RoadsTracks_4 | Minor or local road |
| 3 | FIPS_I_RoadsTracks_2 | A Road |
| 4 | FIPS_I_RoadsTracks_1 | Motorway |
| 5 | FIPS_I_RoadsTracks_5 | Access roads or Track |
| 6 | FIPS_I_RoadsTracks_3 | B Road |
| 7 | FIPS_I_NationalCycleNetwork_1 | On Road: Paved Surface |
| 8 | FIPS_I_NationalCycleNetwork_2 | Traffic Free: Unpaved Surface |
| 9 | FIPS_I_NationalCycleNetwork_3 | Traffic Free: Paved Surface |
| 10 | FIPS_I_NationalCycleNetwork_4 | On Road: Unpaved Surface |
The infrastructure component is derived from a raster indicating the presence or absence of infrastructure features. This original raster, labeled “original” in the Easter Bush example map, contains: 1 for cells where the feature is present, and 0 or NAs for cells where the feature is absent. CHRISTOPHER TO ADD WHERE THE ORIGINAL RASTER CAME FROM.
To account for proximity effects, the distance from each cell to the nearest infrastructure feature was calculated using the terra::distance() function in R. The resulting raster, labeled “distance” in the example map, contains the distance in meters from the center of each cell to the nearest feature.
Because this operation is memory-intensive at the national scale, the original Scotland-wide raster was divided into 20 spatial “windows”.To ensure accuracy near window boundaries, each window was processed with an additional 10 km buffer, allowing distances to be computed to features located in neighboring windows.
Finally, considering only the cells that were <= 500m distance from a feature (everything with features further away than that got assigned a NAs), the values were re-scaled from 0 to 1 (1 = present in cell, and then decreasing score as you get away from that cell). This raster is named “scored” in the example map, using the distance decay function JOE TO ADD PAPER FOR THIS FUNCTION + kappa and alpha values:
\[ distance (m) = \frac{\kappa + 1}{\kappa + \exp(\alpha x)} \]
Where kappa has a value of XX and alpha has a value of XX.
The “scored” raster is what it is utilized by the RP model.
- Water component
The water component is derived from a raster indicating the presence or absence of infrastructure features. This original raster, labeled “original” in the Easter Bush example map, contains: 1 for cells where the feature is present, and 0 or NAs for cells where the feature is absent. CHRISTOPHER TO ADD WHERE THE ORIGINAL RASTER CAME FROM.
Then, using the same methodology applied to the infrastructure component’s raster the “distance” and ”scored” raster were created.
The “scored” raster is what it is utilized by the RP model.
| nr | Name | Description |
|---|---|---|
| 1 | Water_Lakes_1 | Pond |
| 2 | Water_Lakes_2 | Lochan |
| 3 | Water_Lakes_3 | Small Lochs |
| 4 | Water_Lakes_4 | Medium Lochs |
| 5 | Water_Lakes_5 | Large Lochs |
| 6 | Water_Lakes_6 | Major Lochs |
| 7 | Water_Rivers_1 | Minor river or tributary |
| 8 | Water_Rivers_2 | Unnamed minor stream or tributary |
| 9 | Water_Rivers_3 | Major river or tributary |
| 10 | Water_Rivers_4 | Named minor stream or tributary |
| 11 | Water_Rivers_5 | Lake |
| 12 | Water_Rivers_6 | Tidal river or estuary |
| 13 | Water_Rivers_7 | Canal |
3. The Shiny app for the “watches” spin off
JOE TO ADD SECTION, MAYBE PROVIDE LINK?
4. Compertamentalization
JOE TO ADD SECTION, I HAVE NO IDEA WHAT WAS DONE HERE
5. Running the model for the whole of Scotland
MADDA TO ADD SECTION ONCE MODEL HAS RUN
6. Lessons learned
- Transitioning to terra::SpatRaster Improved Performance
One of the most significant improvements came from switching from the legacy raster::Raster to the more modern and efficient terra::SpatRaster format. This change brought several key benefits:
Faster processing of raster operations such as cropping, masking, and mathematical transformations.
Better memory management, particularly for large datasets covering all of Scotland with a high resolutions.
Improved integration with spatial vector formats and compatibility with modern spatial workflows in R.
Overall, the use of the package terra reduced computational overhead significantly (see report “Optimizing BioDT Recreational Potential (RT) model run-time”).
- Pre-processing at the National Scale Greatly Reduces Run-time
Pre-processing component rasters at the national (Scotland-wide) scale, rather than performing these operations at run-time for each user-defined area. Pre-processing included:
Standardizing spatial properties (extent, resolution, projection) across all four component rasters to ensure seamless overlay and computation.
Pre-computing distance rasters, to avoid repeated and costly distance calculations during model runs.
Pre-calculating scored rasters using predefined thresholds and decay functions, making them immediately usable for user-defined queries.
Overall, the use of this approach has reduced computational overhead significantly (see report “Optimizing BioDT Recreational Potential (RT) model run-time”).
- JOE to add lessons on shiny/compartmentalization if any
- CHRIS to add any other lessons if he has any
6. Future improvements
As the RP Model was developed as a prototype, several enhancements could improve its usability, accuracy, and performance. The following areas have been identified for future development:
Simplifying feature scoring
Currently, the model requires users to score 87 individual features across the four components. While this allows for a detailed definition of the “persona profile”, it can be time-consuming for users. A future version could:
Group similar features into broader thematic categories (e.g., “mountain terrain,” “urban infrastructure,” “wetlands”), allowing users to score these aggregated categories rather than individual layers, making the scoring process more intuitive and efficient
Maintain the option to score each of the 87 featre for expert users who want fine control
A simpler model configuration could make the model accessible to more users.
Connecting to Live, regularly updated data-sets
Currently the model relies on static raster layers to represents the components, which may become outdated as landscapes change or as better data becomes available. A future version of the model could:
- Link the model to live/regularly updated data-sets
- Automatically generate the “original” raster layers for each component when updated data becomes available
User-defined weighting components
Currently the RP model assumes that all four components contribute equally to the Recreational Potential score. However, not all personas may value these components equally. A future version of the model could:
- Allow users to specify custom weights for each of the four components (e.g. ,50% landscape, 20% water, 15% infrastructure and 15% natural features)
Expanding the number of Persona presets available
To make the RP model more accessible to casual non-technical users, and to encourage exploration and experimentation with it, a future version of the model could:
- Provide persona preset for common user types (e.g., hikers, birdwatchers, families, cyclists etc.)
- Define both features scores and components weights for each persona
Include seasonal dynamics
Currently, the RP model assumes a static Recreation Potential throughout the year, however, this can vary with seasons, and weather conditions. A future version of the model could:
- integrate temporal layers by asking the persona to specify what season they are intending to recreate in. Then the model could have the underlying components starting values to have different values for different seasons, accounting for trail closures or flooding risks etc.)
Allow for “future scenarios”
With the aim of making the RP model the more relevant to policy makers and stakeholders, a future version of the model could:
- have a “scenario analysis” mode, where users can “add” or “remove” values underlying components (e.g., to simulate a new trail or a new cycle path being added or the change of a landscape due to climate change or socio-economic changes)
- allow for direct comparison on how the two (or more) scenarios differ (provide the user with a decrease or increase of Recreational Potential raster.
JOE and CHRIS to add something if you have other ideas